A DQN Walkthrough with CartPole

The DQN idea#

Q-learning estimates the expected return of taking an action in a state. Deep Q-Networks (DQN) use a neural network to approximate that action-value function, which lets the method work with continuous observations such as CartPole’s position, velocity, pole angle, and angular velocity.

The source article describes three familiar stabilizing components:

1. A Q-network estimates values for the available actions.
2. Experience replay stores transitions and samples batches for learning, reducing dependence on adjacent observations.
3. A target network supplies a slower-moving target for temporal-difference updates.

The action policy balances exploration and exploitation with an epsilon-greedy strategy. The article’s implementation also includes a replay buffer, periodic target-network updates, training and evaluation loops, and plots for scores, losses, and estimated Q-values.

CartPole as a small control task#

CartPole has a four-dimensional continuous observation and two discrete actions: push left or push right. The environment gives a reward for each step the pole remains balanced, so a longer episode produces a higher score. The article’s code sets up a PyTorch network and trains an agent in CartPole-v1.

Results reported by the article#

The Chinese source reports that its agent learned to balance the pole after roughly 800–1,200 training episodes. It states a final average score of 500, about 1,000 training episodes, and an average above 495 across 100 consecutive episodes.

Those numbers are useful as claims to investigate, but the post does not attach the plotted score data, exact package versions, a saved checkpoint, or a run log. It also states that sample efficiency improved “100 times” over a random policy without defining the baseline or calculation. That ratio should not be treated as a reproducible measurement from the information provided.

Parameters and extensions#

The example configuration includes a learning rate, discount factor, epsilon schedule, replay-buffer capacity, batch size, and target-update interval. The source recommends tuning these against training stability rather than treating them as universal settings.

It lists Double DQN, Dueling DQN, Prioritized Experience Replay, and Rainbow as possible extensions. Each changes a part of the learning or sampling process; their effects need to be evaluated on a stated environment and protocol.

What this example can—and cannot—show#

CartPole is a compact way to inspect a value-based learning loop, but success on this task is not evidence of general reasoning, agentic capability, or recursive self-improvement. An RSI-oriented experiment would need to define what improves from one iteration to the next, compare against a fixed baseline, and preserve the configuration and evaluation records.

For the complete Chinese walkthrough—including the PyTorch implementation, training loop, plots, and parameter discussion—see the original article.